基于NAND闪存的神经形态计算的审查.
Sung-Tae Lee1, Jong-Ho Lee2,3
1School of Electronic and Electrical Engineering, Hongik University, Seoul 04066, Republic of Korea. lst777@hongik.ac.kr.
Nanoscale horizons
|July 17, 2024
概括
使用NAND闪存进行神经形态计算,为数据处理提供了节能的解决方案. 本综述详细介绍了离芯片和芯片内学习的架构,重点介绍了NAND闪存.
科学领域:
- 神经形态工程的神经形态工程
- 固态电子 固态电子
- 人工智能 硬件 硬件
背景情况:
- 数据的指数增长需要先进的计算解决方案来克服电力消耗的挑战.
- 神经形态电子,灵感来自生物系统,提供内存计算绕过·诺伊曼瓶.
- 纳德闪存为大规模数据存储和处理提供了具有竞争力的非易失性技术.
研究的目的:
- 为了提供一个全面的概述最近在神经形态计算利用NAND闪存的进步.
- 探索各种神经形态架构,包括基于NAND闪存技术的离芯片和芯片内学习范式.
- 讨论与其在神经网络结构中的应用相关的NAND闪存的基本方面.
主要方法:
- 对使用NAND闪存器的神经形态计算架构的现有文献的审查.
- 分析具有不同输入和权重量化级别的离芯片学习架构.
- 检查使用反向传播和反对齐算法的芯片上学习架构.
主要成果:
- 详细讨论NAND闪存的数组架构,操作方案和神经形态应用的电气特性.
- 在芯片上和离芯片学习实现之间的数组架构差异的比较.
- 在各种神经网络结构中展示NAND闪存的潜力.
结论:
- 基于NAND闪存的神经形态计算提供了一条可行的途径,可以在数据密集型任务中降低功耗.
- 本综述为在神经形态计算系统中利用NAND闪存建立了基础的理解.
- 这些发现为根据特定应用要求利用NAND闪存提供了指导.
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